/content-performance-analysis

name: content-performance-analysis
description: Use when evaluating individual Xiaohongshu post performance, identifying what makes content succeed or fail, extracting viral content patterns, recognizing underperforming content that needs optimization, or comparing performance across different content types and formats

Content Performance Analysis (内容效果分析)

Overview

Content performance analysis is the systematic evaluation of individual posts and overall content portfolio to identify success patterns, understand what resonates with the audience, and make data-driven decisions about content strategy.

When to Use

**Use when**:

• Post performance is inconsistent or unpredictable
• Need to understand why certain content went viral
• Identifying patterns in top-performing content
• Recognizing underperforming content that needs improvement
• Comparing different content formats (carousel vs video vs single image)
• Deciding which content types to focus on
• Planning content optimization based on past performance

**Do NOT use when**:

• Account has fewer than 5 published posts (insufficient data)
• Looking for real-time performance during first hours (wait 3-7 days)
• Analyzing paid advertising performance (use ad analytics tools)

Core Pattern

**Before** (guessing what works):

❌ "This post should do well, I worked hard on it"
❌ "I don't know why this post went viral, lucky I guess"
❌ "All my content is pretty similar, performance is random"

**After** (data-driven content insights):

✅ "Top 5 posts all use carousel format with before/after structure"
✅ "Posts with question titles get 2.3x more comments than statement titles"
✅ "Video content underperforms images - shift strategy to graphic content"
✅ "Posts published on Tuesday outperform Sunday by 40%"

**3 Analysis Dimensions Framework**: 1. **Engagement Data** - Likes, comments, shares, saves (audience response) 2. **Growth Data** - New followers, profile visits (conversion impact) 3. **Viral Data** - Exposure, discovery traffic (reach and algorithm favor)

Quick Reference

| Metric | What It Reveals | Good Benchmark | Analysis Method | |--------|----------------|----------------|-----------------| | **Engagement Rate** | Content resonance | 8-12% average | (Likes+Comments+Shares+Saves)÷Views×100 | | **Save Rate** | Content value/reuse | 3-5% is good | Saves÷Views×100 | | **Comment Rate** | Discussion spark | 2-4% average | Comments÷Views×100 | | **Follower Conversion** | Content converts to fans | 1-3% | New Followers÷Views×100 | | **Viral Score** | Algorithm favor | Views÷Followers | >10 = viral hit |

Implementation

Step 1: Collect Post Performance Data

**From Xiaohongshu Creator Center**: 1. Open Creator Center → 内容数据 2. Select time range (last 30 days recommended) 3. Export or manually record data for each post:

• Title
• Content type (image/video/carousel)
• Publish date/time
• Views (浏览量)
• Likes (点赞数)
• Comments (评论数)
• Shares (转发数)
• Saves (收藏数)
• New followers gained

**From Qiangua Data** (recommended for efficiency): 1. Account analysis → Content performance 2. Export all posts with metrics to Excel 3. Sort by different metrics to identify patterns

Step 2: Calculate Key Performance Indicators

For each post, calculate:

**Engagement Rate**:

Engagement Rate = (Likes + Comments + Shares + Saves) ÷ Views × 100

• **Excellent**: >15%
• **Good**: 8-15%
• **Average**: 5-8%
• **Below Average**: <5%

**Save Rate** (content value):

Save Rate = Saves ÷ Views × 100

• **Excellent**: >7%
• **Good**: 4-7%
• **Average**: 2-4%
• **Low**: <2%

**Comment Rate** (engagement depth):

Comment Rate = Comments ÷ Views × 100

• **Excellent**: >5%
• **Good**: 3-5%
• **Average**: 1-3%
• **Low**: <1%

**Viral Score**:

Viral Score = Views ÷ Follower Count

• **Viral Hit**: >10 (reached 10x beyond existing audience)
• **Strong Performance**: 5-10
• **Expected Performance**: 1-5
• **Underperforming**: <1

Step 3: Identify Top and Bottom Performing Content

**Top Performers** (analyze last 10-20 posts): 1. Sort by **Engagement Rate** - Find top 5 2. Sort by **Viral Score** - Find top 5 3. Sort by **Save Rate** - Find top 5

**Bottom Performers**: 1. Sort by **Engagement Rate** - Find bottom 5 2. Identify posts with **Viral Score <1** (underperformed existing audience)

Step 4: Extract Success Patterns from Top Content

Analyze top 5 posts for common patterns:

**Content Format Patterns**:

• Single image vs carousel vs video
• Carousel slide count (3-5 slides optimal)
• Video length (under 60 seconds optimal)

**Content Structure Patterns**:

• Hook/intro style (question, statement, before/after)
• Main content organization (list, tutorial, story, comparison)
• Call-to-action presence and type

**Title Patterns**:

• Question vs statement
• Length (short vs long)
• Keyword usage
• Emotional triggers (curiosity, urgency, benefit)

**Visual Patterns**:

• Cover design style
• Color scheme
• Text overlay presence
• Face presence vs product-only

**Topic Patterns**:

• Content category (educational, entertainment, inspiration)
• Specific subtopics
• Target audience segment

**Timing Patterns**:

• Day of week
• Time of day
• Seasonal relevance

**Document findings**:

Pattern: Carousel format
- Frequency in top 5: 4/5 posts (80%)
- Average engagement: 14.2%
- Common structure: Before/after transformation

Pattern: Question-based titles
- Frequency in top 5: 3/5 posts (60%)
- Average engagement: 13.8%
- Comment rate: 4.1% (above average)

Step 5: Diagnose Underperforming Content

For bottom 5 posts, analyze:

**Content Quality Issues**:

• Low production value (poor images, bad lighting)
• Unclear value proposition
• Weak or confusing message
• Inadequate detail or depth

**Content Format Issues**:

• Suboptimal format for topic (e.g., complex topic in single image)
• Wrong carousel length (too short or too long)
• Video length issues (too long